Addressing the Unit of Analysis in Medical Care Studies
Bibliographic record
Abstract
OBJECTIVE: We assessed the frequency that patients are incorrectly used as the unit of analysis among studies of physicians' patient care behavior in articles published in high impact journals. METHODS: We surveyed 30 high-impact journals across 6 medical fields for articles susceptible to unit of analysis errors published from 1994 to 2005. Three reviewers independently abstracted articles using previously published criteria to determine the presence of analytic errors. RESULTS: One hundred fourteen susceptible articles were found published in 15 journals, 4 journals published the majority (71 of 114 or 62.3%) of studies, 40 were intervention studies, and 74 were noninterventional studies. The unit of analysis error was present in 19 (48%) of the intervention studies and 31 (42%) of the noninterventional studies (overall error rate 44%). The frequency of the error decreased between 1994-1999 (N = 38; 65% error) and 2000-2005 (N = 76; 33% error) (P = 0.001). CONCLUSIONS: Although the frequency of the error in published studies is decreasing, further improvement remains desirable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.522 | 0.835 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".